Hybrid Database System for Dynamic Query Engine Selection
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Solution Overview
Problem
Current data management systems face performance issues due to reliance on single data models, such as graph or relational databases, which are suboptimal for certain query types, leading to inefficient query execution and requiring manual expertise for data structure selection, making it laborious and prone to performance degradation with evolving inputs.
Innovation Solution
A hybrid database system that automatically generates relational databases from graph databases, using machine learning to predict the optimal query engine (graph or relational) for query execution, thereby selecting the most suitable data representation for each query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single data model (graph or relational) is used, then the system is simple to operate, but query execution performance deteriorates for certain query types
Solution Approach 1:
The system implements multiple data models (graph and relational) within a single database system, allowing it to handle different query types optimally. The graph model handles relationship-intensive queries while the relational model handles attribute-intensive queries, making the system universally capable of handling diverse query workloads efficiently
2Productivity
If manual expertise is used for data structure selection, then query execution performance can be optimized, but the process becomes laborious and prone to degradation with evolving inputs
Solution Approach 1:
The system automatically selects between graph and relational data models using machine learning classification, eliminating the need for manual expert intervention. The classifier analyzes query characteristics and autonomously determines the optimal data model, making the system self-sufficient and adaptable to evolving inputs without requiring ongoing manual expertise
3Device complexity
If a single data model is used, then device complexity is reduced, but adaptability to different query types deteriorates
Solution Approach 1:
The system dynamically selects between graph and relational data models based on the specific query characteristics rather than being fixed to a single model. This dynamic adaptation allows the system to optimize performance for different query types (relationship-intensive vs. attribute-intensive) while maintaining a unified database architecture that manages complexity internally
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture for providing query selection systems are disclosed. An example query selection system includes a processor to: analyze a graph database; identify respective ones of objects associated with the graph database; obtain properties associated with the objects; identify common properties present in the respective ones of the objects; in response to determining the common properties present in the identified objects, output a list of the common properties corresponding to the respective ones of the objects; generate a table for the common properties and the respective ones of the objects; and populate the table with the common properties and the respective ones of the objects from the graph database to establish a relational database. The system further includes a machine learning model classifier to enable relational database query searching via the relational database.